Berkeley parser obtains the smallest gains from fusion since Berkeley's n-best lists are ordered by factors other than probabilities.

the probabilities from Berkeley can mislead the fusion process.


It is possible that a parser's n-list and its scores act as a weak approximation to the full parse forest.

fusion seems to provide part of the benefits seen in forest reranking (Huang, 2008).


These additional non-monotonic transitions permit the parser to modify what would have been irrevocable parsing decisions in the monotonic arc-eager system when later information justifies this action.

one effect of adding the non-monotonic parsing transitions is to effectively delay the location in the input where the parser must ultimately commit to a particular attachment.


In contrast, a large amount of unlabeled data are easily available.

can we mine syntactic or semantic properties for nonterminals from unlabeled data?


The approach (MV + CS) achieves better performance and it is more efficient than (MD + ED) where the computation of semantic centroids is time-consuming.

we adopt the approach (MV + CS) with α = 0 to learn semantic vectors for nonterminals and compute semantic similarities in the following experiments.


All other source words f , that are also aligned to e, are processed as if they were aligned to the artificial word σ .

each of these f generates a token f , σ .


For example, the average length of an aspect term in our Restaurant dataset is 1.4, where the average length of an expressive subjective expression in their MPQA corpus is 3.3.

the information required to correctly identify aspect terms (e.g., hard disk) is already captured by the simple (as opposed to LSTM) unidirectional link and the context window covering the neighboring words.


6 As a partial solution to this problem, we experimented with a bi-directional Elman-RNN, where both directions share the same parameters.

the number of parameters remains the same as the uni-directional one.


The key observation is that our supertagging, labelling and dependency features can each be associated with exactly one word (using the functor for the dependency features).

we can equivalently view the complete parse as a series of extended lexical entries: y = y 0 ...y N .


For example, in Figure 3, the partial parse of the span confirm or deny reports, the extended lexical entries for the words outside the span (He, refused and to) are completely unspecified.

the extended lexical entries for words inside the span have specified categories, but can contain underspecified dependencies: we can compute an upper bound for the outside score of a partial parse as a sum of the upper bounds of the unspecified components of each extended lexical entry.


SMT alignment models are typically trained in unsupervised fashion, inducing lexical correspondences from massive quantities of sentencealigned bitexts.

the symmetrization of word alignments may not work perfectly for semantic parsing.


5 The advantages include: first, considering more possible alignments would increase the phrase coverage, especially when the training data is little; second, including the alignment from both directions would alleviate the error propagation caused by mis-aligned stop words (e.g., be, the in NL and stateid@1 in MRL ).

the phrase extractor will include phrase pairs of both highest, largest one@1 and texas, texas@s .


9 This is not surprising since a phrase with non-terminal X (e.g., the X on the source side) may be further specified as multiple phrases with various nonterminals (e.g., the C, the C/A1, etc.).

the average number of phrases per sentence (in English test data, hereafter) increases from 453 to 893 while the decoding time of the SMT decoder increases from 0.11 seconds to 0.19 seconds per sentence on average.


However, due to the constraint that each target phrase needs to map to a syntactic constituent, phrase tables in tree-based translation models usually suffer from the low coverage issue, especially if the training data size is small.

another direction of our future work is to explore specific problems that will emerge when employing tree-based SMT systems to semantic parsing, and provide solutions to them.


For official questions, GEOS answers 27 questions correctly, 1 questions incorrectly, and leaves 27 un-answered, which gives it a score of 26.75 out of 55, or 49%.

gEOS's precision exceeds 96% on the 51% of questions that it chooses to answer.


We model the joint expected utility of a d in writing θ d as the average of the group's utility.

1 where the "cost" term is scaled by c d,a , denoting the fractional "contribution" of author a to document d. a∈a d c d,a = 1, and we treat c d as a latent categorical distribution to be inferred.


However, it is manifestly clear that similarity in form is neither a necessary nor sufficient condition for similarity in function: small orthographic differences may correspond to large semantic or syntactic differences (butter vs. batter), and large orthographic differences may obscure nearly perfect functional correspondence (rich vs. affluent).

any orthographically aware model must be able to capture non-compositional effects in addition to more regular effects due to, e.g., morphological processes.


Formally, such models define a matrix P ∈ R d×|V | , which contains d parameters for each word in the vocabulary V .

for a given word type w ∈ V , a column is selected by right-multiplying P by a one-hot vector of length |V |, which we write 1 w , that is zero in every dimension except for the element corresponding to w. P is often referred to as word lookup table and we shall denote by e W w ∈ R d the embedding obtained from a word lookup table for w as e W w = P • 1 w .


We define a projection layer P C ∈ R d C ×|C| , where d C is the number of parameters for each character in the character set C. This of course just a character lookup table, and is used to capture similarities between characters in a language (e.g., vowels vs. consonants).

we write the projection of each input character c i as e Given the input vectors x 1 , .


Relative to e W w , computing e C w is computational expensive, as it requires two LSTMs traversals of length m. However, e C w only depends on the character sequence of that word, which means that unless the parameters are updated, it is possible to cache the value of e C w for each different w's that will be used repeatedly.

the model can keep a list of the most frequently occurring word types in memory and run the compositional model only for rare words.


The softmax is still simply a d × V table, which encodes the likelihood of every word type in a given context, which is a closed-vocabulary model.

at test time out-of-vocabulary (OOV) words cannot be addressed.


There are approximately 40k lowercased word types in the training data in the PTB dataset.

a word lookup table with 50 dimensions per type contains approximately 2 million parameters.


In the C2W models, the number of characters types (including uppercase and lowercase) is approxi-  mately 80.

the character look up table consists of only 4k parameters, which is negligible compared to the number of parameters in the compositional model, which is once again 150k parameters.


We propose a C2W model that builds word embeddings for words without an explicit word lookup table.

it benefits from being sensitive to lexical aspects within words, as it takes characters as atomic units to derive the embeddings for the word.


It is a merit of this proposal that, in cases like these, it is possible for one to train the generic model of Figure 2 on any large corpus of text, and then use the produced word vectors and compositional weights to initialize the parameters of a more specific version of the architecture.

the trained parameters will be further refined according to the task-specific objective.


The advantage of our approach against such models is twofold: firstly, the word embeddings are accompanied by a generic compositional model that can be used for creating sentence representations independently of any specific task; and secondly, the training is quite forgiving to data sparsity problems that in general a dependency-based approach would intensify (since context words are paired with the grammatical relations they occur with the target word).

a small corpus such as the BNC is sufficient for producing high quality syntax-aware word embeddings.


However, even small differences on those aspects between two sentences can lead to a different classification result.

we train (using the MSPRC training data) an additional logistic regression classifier which is based not only on the embeddings similarity, but also on a few handengineered features.


In each case, the T node on the right-hand side of these rules generates the first post (in terms of posting time) in that subtree.

posts made earlier in time always dominate (in the tree structure) those which come later in the thread.


For Chinese news categorization, there is no widely-used publicly available word vectors.

we run word2vec (Mikolov et al., 2013) to train 200-dimensional word vectors on the 1.6 million Chinese news articles.


We use POS embeddings only for the MSRP task.

for MSRP, we concatenate all word and POS embeddings and obtain Dim = Dim g + Dim p + Dim k = 525-dimension vectors for each input word; for SICK and MSRVID we only use Dim = 300-dimension GloVe embeddings.


However, computing the label frequencies P(y = 1|q) requires an infinite amount of data.

approximation methods are required to perform calibration analysis.


We wish v s j v s i in (1) captures as much correlations between s i and s j as possible.


Because r t can only be increased to γ + 1 or set to 0, the conditional probability is as follows: is the joint probability of the run length r t−1 and a set of documents D (γ) t when no change has occurred at time t and the run length becomes γ + 1.

we can simplify the equation by removing r t−1 =γ from the condition as follows: length parameter r t .


With the predicted run length calculated in Section 3.3, we change the run length domain r ∈ R into 0 ≤ r ≤ 1 by predicting e rt rather than r t to solve the interpretability problem.

we can think of a high weight w i as a powerful word which changes the current run length r to 0.


We think this connection is very useful for RTE.

the predicates in the T -H pair should be matched to the YAGO facts for making advantage of the connection information.


For each word to be labeled, we adopt linear transformation to get the output vector o t : where n 4 is the number of semantic role labels in IOBES tagging schema.

the resulting vector o t for the t-th word is of length n 4 , and each dimension corresponds to the score of a certain semantic role label.


The word layer can well capture the relatedness between words, but just partially model the negation focus since it is more directly related with topic than content.

we add one more layer to refine the graph model over the topical information, as shown in Figure 1.


Finally, according to the annotation guidelines (Blanco and Moldovan, 2011), the focus is always a full text of a semantic role.

we select all of semantic roles in sentence as candidate focuses for ranking.


As a side note, the Lexfunc implementation in DISSECT does not produce a composite representation for 11.5% of the our test data, where a word does not appear as a modifier during training.

we reimplemented the Lexfunc model and solved the missing training material problem by initializing the matrix for all the words in the dictionary with I + , the identity matrix plus a small amount of Gaussian noise.


Aggregate agreement for both tasks improve with larger k, but returns quickly diminish.

we chose k = 5 for the full data collection to reasonably trade off between quality and quantity.


The highest overlap score between a story text window and the question-answer pair is taken as the score for the answer.

their algorithm makes a single pass over the story text per answer.


The entities mentioned in MCTest stories are frequently referred to by anaphoric expressions throughout the story and the questions, which is ignored by the described scoring function.

we substituted each mention in a coreference chain with its representative mention, applied the scoring function on the processed text and added the score to the original one.


We found that coreference improved performance on some question types, but decreased performance on others.

we developed a set of question rules in order to apply it selectively, which we discuss in the next section.


However, in contrast to Collins and Roark (2004) our data does not include gold derivations.

we attempt to identify max-scoring partial derivations that may lead to the correct derivation.


We therefore adopt Stanford's Contextual Word Similarities (SCWS) (Huang et al., 2012), in which human judgments are associated with pairs of words in context.

for example "bank" in the context of "river bank" would have low relatedness with "deficit" in the context "financial deficit".


As far as possible we try to perform an appleto-apple comparison on these tasks, and our goal is an analytic one-to investigate how well semantic information can be encoded in multi-sense embeddings and how they can improve NLU performances-rather than an attempt to create state-of-the-art results.

for example, in tagging tasks (e.g., NER, POS), we follow the protocols in (Collobert et al., 2011) using the concatenation of neighboring embeddings as input features rather than treating embeddings as auxiliary features which are fed into a CRF model along with other manually developed features as in Pennington et al.


A feed forward neural network is a universal approximator (Cybenko, 1989): feed-forward network with a single hidden layer can approximate any continuous function, provided it has enough hidden units.

we use neural networks as a powerful class of learning models to learn semantic composition.


The numeric value of the quantity associated with a leaf node n is denoted as Q(n).

each node n also has a value associated with it, represented as VAL(n), which can be computed in a recursive way as follows: if n is a leaf VAL(lc(n)) (n) VAL(rc(n)) otherwise (1) For any expression tree T for expression e with root node n root , the value of VAL(n root ) is exactly equal to the numeric value of the expression e. this gives a natural representation of numeric expressions, providing a natural parenthesization of the numeric expression.


In the rest of the section, we show that for any pair of quantities q i , q j in the solution expression, any monotonic tree for the solution expression has the same LCA operation.

predicting the LCA operation becomes a multiclass classification problem.


The opposite transformation adds same term to various expressions leading to multiple uses of the same quantity.

this will force at least one of C 1 and C 2 to use the same quantity more than once, violating validity.


Positive Answer: Most arithmetic problems asking for amounts or number of objects usually have a positive number as an answer.

while searching for the best scoring expression, we reject expressions generating negative answer.


We refer to each of the relevant quantities in each state as a term.

each state can be represented as a set of terms.


In total, there were 600 problems, 100 for each of the following types: This dataset had no irrelevant quantities.

we did not use the relevance classifier in our evaluations.


McDonald and Nivre (2007) showed that the accuracy of syntactic parsing decreases significantly with increasing sentence length.

when using traditional features, the problem of error propagation or accumulation will not only exist, it will grow more serious.


In relation extraction, an input sentence that is marked as containing the target entities corresponds only to a relation type; it does not predict labels for each word.

it might be necessary to utilize all local features and perform this prediction globally.


A manual check of the misclassified examples that were produced with high confidence reveals that the ma-jorities of these examples are false negatives and are actually true relation instances that were misclassified due to the incomplete nature of Freebase.

the held-out evaluation suffers from false negatives in Freebase.


In more detail, we (conceptually) build a negative sample set X − r for each relation r ∈ R. We include into X − r all facts r(t)-again, along with their context-such that t ∈ T is an observed tuple but r(t) is an unobserved fact.

the subject-object pair t of entities is not observed with relation r in the input data (but with some other relation).


On the other hand, if the embedding is similar but is not on the dependency path, it will have 0 weight.

our model generalizes its model parameters across words with similar embeddings only when they share similar functions in the sentence.


We address this issue by having γ G depend on the number of observed types of features in the group: where type(G, t) is the number of different kinds of features seen in that group (e.g., DOG and CAT are two different feature types from the same group) up through time t. In this way, the P u t of a feature that occurs in a group with more observed feature types is higher than the P u t in a group with fewer observed types.

both the type frequency of features in G and their token frequency of co-occurrence with word w j will influence -the first positively and the second negatively -how readily w j can refer to objects with previously unseen features from G. We model X&T's behavioural experiments with our computational word learner as extended above.


In other words, the leave-one-out method would align rare words unless the confidence is high.

we plan to seek a method to integrate Kneser-Ney smoothing into the proposed leave-one-out method in the future work.


This makes the posteriors to be sharply peaked around the Viterbi alignments (Liang et al., 2006).

the lack of many-to-many alignments limits the benefits of joint training to end-to-end machine translation.


Prior work has shown that the language statistic information is important for text normalization Li and Yarowsky, 2008;Kaji and Kitsuregawa, 2014).

we extract language model features by using word-based language model learned from a large quantity of standard texts.


No prior knowledge about feature interactions is assumed.

the model considers all possible combinations of atomic features, and addresses the parameter explosion problem via a low-rank assumption.


Therefore, the embedding T u φ tu captures an expert-defined interaction over the head, the modifier and the arc.

e 1 and T u φ tu provide two different representations of the same set of atomic features (e.g.


the head) and our prior knowledge motivates us to exclude the interaction between them since the low-rank assumption would not apply.

we combine e 1 and T u φ tu as e 2 using an element-wise sum and thereby avoid such combinations.


It is over the same set of atomic features as the typological features that depend on arc labels φ t l , such as the example verb-subject ordering feature in Table 1.

we sum over these embeddings as Finally, we capture the interaction between e 4 and context feature embeddings H c φ hc and   (5) By combining Equation 1 to 5, we observe that our hierarchical tensor score decomposes into three multiway tensor scoring functions.


The other two sets of features f 1 and f 2 focus on combining atomic typological features with atomic label and context features.

we explicitly assign zero weights for invalid assignments, by excluding the combination of φ tu with φ h and φ m .


As described in Section 3.2, we can view our hierarchical tensor as the combination of three multiway tensors with parameter sharing.

we can compute the gradient of each multiway tensor and take the sum accordingly.


To reduce the noise from the automatic projection process, we only incorporate lexical information for the top-100 most frequent words in the following closed classes: pronoun, determiner, adposition, conjunction, particle and punctuation mark.

we call this feature extension partial lexicalization.


This multilingual treebank is annotated with a universal POS tagset and a universal dependency label set.

this dataset is an excellent benchmark for cross-lingual transfer evaluation.


The main argument behind the use of such a diverse set of solutions (such as k-best list of parses for a natural language sentence) is the hope that each solution in the set is mostly correct.

recombination or reranking of solutions in that set will further optimize the choice of a solution, combining together the information from all solutions.


Besides, the standard RNN can just deal with the binary tree.

we cannot apply the standard RNN directly.


Each hidden neuron in two proposed GRNNs can be regarded as a different combination of the input features.

the whole model has an ability to simulate the design of the sophisticated feature combinations in the traditional discrete feature based methods.


The latent word is represented as a specific word that is selected from the entire vocabulary.

the number of latent words equals the number of observed words.


Our basic assumption is domain-matched training data is not available.

for LM training, we used the Corpus of Spontaneous Japanese (CSJ) whose domain is a spontaneous lecture task (Maekawa et al., 2000).


In fact, when we generated one billion words using a trained LWLM or trained h-LWLM, the number of observed trigrams in h-LWLM with 5 layers was 101M while the number of observed trigrams in non-hierarchical LWLM was 94M.

h-LWLM can generate unseen words unlike nonhierarchical LWLM.


While vocabulary and lyrical content are key components for an artist's style, this is inherently satisfied by using words only from the training data.

rhyme style -specifically rhyme density -will be the key performance indicator for imitating an artist's style.


As we could conclude from our experiments, the best summarization results are obtained if this parameter is set to the maximal number of words in the summary, W .

we used 100 codes for summarizing DUC 2002 and DUC 2004 documents and 250 codes for DUC 2007 documents.


Our experiments showed that allowing a small gap between words of a sequence helps to improve slightly the ranking of sentences, but the improvement is not significant.

we used Gap = 0.8 in comparative experiments for all corpora.


Both syntactic and semantic features contribute to the success of a RE.

we construct a joint model for predicting SR RE using step-wise linear regression over the joint feature space.


For example, Twitter, one of the most widely adopted social media platforms, appears to cover nearly all newswire events (Petrovic et al., 2013).

approaches have also been proposed for storyline summarization on social media.


The final summary should contain only important sentences.

we give a score to every sentence in a document to obtain important sentences.


Equation 6 shows that topics present in sentence x i are selected, when sentence x i is selected.


If topic y j = 1, then at least one sentence containing this topic has been selected.

• They are accompanied by an authors' abstract.


We assume the phrases in a cluster are semantically similar to each other and any phrase in a cluster can represent it as a whole.

the coverage of a phrase is assumed to be the same as the coverage of a cluster, which is a union of the students covered by each phrase in the cluster.


For simplicity, we use the following two abbreviations: C s := C ∪ {s} and  Therefore, we obtain f NN C (s) ≥ 0 from the nondecreasing property of g. (Submodularity) Next, we prove the submodularity.

for any two sets C 1 and C 2 of sentences such that C 1 ⊂ C 2 ⊂ D, and sentence s ∈ D \C 2 , we have we obtain α ≥ 0 from the non-decreasing property of g. If The objective function f NN is simply heuristic for small documents, but the next theorem shows that f NN is asymptotically related to an approximation of KL-divergence in a continuous space, if g is a logarithmic function.


Given an input to parse, simple heuristics are used to select a finite list of potential logical forms which are then ranked according to the (paraphrase-based) similarity of their associated canonical realization with the input.

in this approach, a form of generation plays an important role, not for its own sake, but as a tool for semantic parsing.


The manual annotations from (Clark and Paivio, 2004) contain ratings for only 925 words, which are not enough to provide sufficient coverage.

we use a lexicon expansion method, inspired by the work of (Malandrakis et al., 2013) to estimate the gender ladennessĝ(w i ) of word w i using the semantic similarities s() between w i and reference words or concepts c j , aŝ where the terms θ i are trained model parameters.


TP values are fractions indicating the percentage of a given topic among all topics covered in a particular year.

these values are higher for topics that are the major focus in the venue for a particular year .


For example: Question 4 The process that changes a gas to liquid is called (A) condensation (B) melting (C) evaporation (D) vaporization Because our method ignores word order (the knowledge graph is initially populated with keywords in the question), the representation is inherently incapable of capturing sequential information (e.g., here, gas to liquid vs. liquid to gas).


The sub-units carry meaning and, importantly, they are shared with many other individuals.

our approach is to leverage automatic decomposition of usernames into sub-units for use in classification.


Furthermore, the procedure is targeted so that only paths that play a part in inferring the relations that are of interest are added.

the number of paths added in this manner is much lower than the number of surface relations added using the procedure in (Gardner et al., 2013).


For example, partof-speech annotations extracted from a treebank would yield linguistic vectors capturing syntactic content of vectors.

qVEC can be used as a task-specific evaluator; we will investigate this in future work.


2 A first-order language can be taken as a fragment of this language.

adopting a higher-order language does not lead to the loss of the expressive power of the first-order language.


Frame-evoking words typically belong to a narrow range of part of speech.

we only consider words as target candidates when tagged with one of the top k part-of-speech tags most commonly seen as targets in the training set.


the context of w is defined as all words that are either the head or dependents of w. We will call this approach dep.


A one-versus-all linear classifier learns a linear function f c ∈ R n → R for each class c ∈ C. The classifier assigns to a vector X the best matching class based on these functions: Due to the linearity of the functions f c , the vector space is partitioned into convex polytopes, which each represent exactly one class (see Appendix A).

the classification accuracies can also be interpreted as supervised clustering accuracies.


Because no context information is used during classification, the word vector corresponding to a word will always be classified the same, even though the correct classification might depend on the context, e. g. the word put can belong to different tense classes depending on the context.

an upper bound for the classification task is to assign each word the most probable class for that word (computed on the training set).


Then, by definition, f o (z) > f c (z) for some o ∈ C \ c. We can substitute z with its definition: And therefore due to the linearity of f o and f c : But this cannot be, as by definition, f o (x) < f c (x) and f o (y) < f c (y).

there is only one region for c and that region is convex.


E4 is a pronoun, and thus may seem to refer to an entity rather than an event.

e2 and e4 are relatively difficult to be recognized as events by themselves.


However, event coreference can be explored only from complete assignments of an event trigger.

one would need to synchronize the search process of event coreference by comparing event coreferences from the complete assignment at a certain position with those from complete assignments at following positions.


Therefore, we maximize C satisfying the constraint that Out of the V − C rare words, we would like to pack them into a complete tree so that they may be encoded using our remaining W − C symbols.

we maximize C satisfying the constraint that No-Repeats.


Because S + T + C = W , we have that 2S + C = W .

we maximize C satisfying the constraint that We trained a public implementation 1 of the system described in Bahdanau et al.


2 In this paper, we propose a new framework of the binarized NNJM (BNNJM), which is similar to the NNJM but learns not to predict the next word given the context, but solves a binary classification problem by adding a variable v ∈ {0, 1} that stands for whether the current target word t i is correctly/wrongly produced in terms of source context words s The BNNJM is learned by a feedforward neural network with m + n inputs s Because the BNNJM uses the current target word as input, the information about the current target word can be combined with the context word information and processed in the hidden layers.

the hidden layers can be used to learn the difference between correct target words and noise in the BNNJM, while in the NNJM the hidden layers just contain information about context words and only the output layer can be used to discriminate between the training data and noise, giving the BNNJM more power to learn this classification problem.


The development and test sets were both provided for the CE task while only the test set was provided for the JE task.

we used the sentences from the NTCIR-8 JE test set as the development set.


During the experiments, we tried to make the comparison as fair as possible.

we think that the results of this work can be generalized to similar scenarios.


These differences call for dialect-specific processing and modeling when building Arabic automatic speech recognition (ASR) systems or machine translation (MT) systems.

identification and classification of Arabic text is fundamental for building social media Arabic speech and language processing systems.


The approach used in this work aims at the detection of expressions of suggestions, regardless of the presence or absence of context in the same sentence.

we currently ignore the problems associated with using sentences as a unit for suggestions.


Each statement also bears a unique identity no., which is constituted of hotel identity no., statement number and review identity no.

the reviews belonging to same hotel, and the statements of the same review can be identified.


Heuristic Features: A general notion about the task is that suggestions contain some distinctive keywords like, suggest, recommend etc, and should be easily detectable using them.

we use a set of manually selected features in order to test this notion.


[  if we look into its context, we can find that [R 2 ] talks about similar things as classified as a follower indicates the higher chance of [R 2 ] being a follower rather than a leader.

context information is important for indicating the messages being leaders or followers.


Due to the nature of leaders, they generally cover more important contents than follows do.

our first summarizer selects contents only from detected leaders.


There is no public editorial repost tree dataset.

we manually selected 10 hot events taking place during January 2nd -July 28th 2014, and then used the PKUVIS toolkit (Ren et al., 2014) to crawl the complete repost trees for all the events given the corresponding original posts.


The reason is that repost trees have complex structure, and editors could hardly reconstruct the repost trees even though they went through all the microblogs.

in evaluation for each tree, we computed the average ROUGE F1 score between the model-generated summary and the three human-generated summaries.


(1) [ Quarterly revenue rose 4.5%, to $2.3 billion from $2.2 billion] arg1 [ For the year, net income tumbled 61% to $86 million, or $1.55 a share] arg2 Automatically classifying implicit relations is difficult in large part because it relies on numerous factors, ranging from syntax, and tense and aspect, to lexical semantics and even world knowledge (Asher and Lascarides, 2003).

a lot of previous work on this problem have attempted to incorporate some of these information into their systems.


An example of this approach are the so-called Brown clusters induced using the Brown hierarchical clustering algorithm (Brown et al., 1992) with the goal of maximizing the mutual information of bigrams.

each word is associated to a binary code corresponding to the cluster it belongs to.


In the spirit of this approach, we add an additional component to the network architecture, capturing the bag-of-words for the entire document.

at the root node we have: with Ψ rst-root defined recursively from Equations 3 and 4, θ indicating the vector of per-word weights, and the scalar γ controlling the tradeoff between these two components.


We found that most arguments consisted of adjacent claims and premises, related by a support or attack relation; premises and claims were rarely nested.

we decided to use a simplified claim-premises scheme (see Figure 1), using the terminology set up in Section 1.


Considering the transition probability at word level, a lattice of word candidates corresponding to the concept sequence can be generated.

sentence generation can be thought of as a problem of finding the word sequence that has the highest probability from the lattice of word candidates, which can be solved by the Viterbi algorithm.


Here, for a given observed information w m obs , the top-K words that correspond to concept C n t and have high probabilities w n t = {w n t1 , w n t2 , • • • , w n tK } are used.

hence, the set of all words for a sequence of concepts C n can be written as K Tn number of patterns for a candidate of the sentence can be considered for C n and the corresponding words W n .


Because many patterns of S n exist, the Viterbi algorithm is applied to cut the computational cost and determine the most probable sentence.

a set of sentences that consists of sentences with the highest probability for each sequence of concepts can be written aŝ We can select the final sentence fromŜ by considering the most probable candidate.


The supporting idea sentences account for more than 53%, while the thesis statements account for only 34% in total.

the distinction between minority thesis statements from overwhelming supporting idea sentences is a major challenge.


Similar to Toutanova and Cherry (2009) (TC), we define an alignment between w and l. Our alignments can be read from an edit tree by aligning the characters in LCS nodes character by character and characters in substitution nodes block-wise.

the alignment of umgeschaut -umschauen is: Each alignment pair constitutes a feature in our model.


However, this approach assigned the same probability to the most common and the most nonsensical (although grammatical) candidates as well.

in some cases these forms showed up in the results.


For this, we applied a linear regression model based on corpus frequency data determined for the lemma and other features of the candidate word (since the actual wordform was not present in the corpus).

for each candidate, its lemma frequency (LEM ), the number of productively applied compounding (CM P ), the number of productively applied derivational affixes (DER), and the frequency of the inflectional suffix sequence returned by the analysis were determined.


Even though, following an appropriate scaling of the scores, the ranked candidates could be used the same way as the entries in the translation table, the system would never select any accented form other than the most probable one, since the language model does not include any of these forms.

only the candidate with the highest relative score was made available to the system.


Their objective of using a language model is to normalize informally spelled words in microblogs.


This is because fair comparison becomes more and more difficult as models get complex (e.g., the number of layers, number of hidden units within each layer, etc.).

most neural models employed in this work are comprised of only one layer of neural compositions-despite the fact that deep neural models with multiple layers give better results.


For a constituency tree, it is not completely obvious how to pool features to more than 3 slots and comply with the aforementioned criteria at the same time.

we regard 3-slot pooling for c-TBCNN is a "hard mechanism" temporarily.


Each group is activated at different time periods.

mT-LSTm can model very long documents as well as short sentences.


Fast-to-Slow (F2S) Strategy Intuitively, when we accumulate the short-term memory to a certain degree, we store some valuable information from the short-term memory into the long-term memory.

we firstly define a fast to slow strategy, which updates the slower group using the faster group.


The upper bound of the number of groups is calculated by where L is the average length of the corpus.

the slowest group is activated at least twice.


In order to have a clear distinction between deceptive and truthful trial videos portraying defendants, the process of labeling the trial relies on the verdict.

clips with a guilty verdict are considered deceptive whereas clips with a non-guilty verdict or exoneration are labeled as truthful.


In our proposed method, instead of performing supervised learning in the original document space based on n-grams, we perform learning in a similarity space.

the key novelty of the method is the transformation from the original document space (DS) to a center-based similarity space (CBS).


Notice that for ds-osvm, it does not make sense to have in-training and not-intraining results because it does not use any training negative data.

there is only one set of results for "Combined," which is duplicated in the table for easy comparison.


In contrast, co-training requires only 15% of the labeled training set to outperform the fully supervised content baseline and 30% of the training set to outperform the fully supervised citation contexts baseline.

using a co-training approach that includes citation contexts as well as the document content can not only increase the performance, but will also significantly reduce the need of expensive labeled instances.


The reported results are statistically significant at p value of 0.05, when the training percentage is between 5 and 35.

we can say that train- ing two separate classifiers, one of each view, yields higher performance compared with training a single classifier that incorporates both views.


Last but not least, there are different types of humor (Raz, 2012), such as wordplay, irony and sarcasm, but there exist few formal taxonomies of humor characteristics.

it is almost impossible to design a general algorithm that can classify all the different types of humor, since even human cannot perfectly classify all of them.


(3) Humor Categorization: Moreover, a fine granularity categorization of humor might aid in understanding humorous language, because humor has different types of manifestations, such as irony, sarcasm, creativity, insult and wordplay.

more sophisticated techniques in modeling phrase meaning, external knowledge, humor types, etc., are needed to better expose and define humor for automatic recognition and extraction.


LDA does not explicitly account for the background distribution; to account for this, it is common practice to threshold the vocabulary a priori to remove very common and very rare words (though in our experiments, we do not do this).

φ (d,n) is exactly , and H k ∼ Dirichlet (η).


The words the and former are modifiers of president and they get filtered out.

we obtain a less specific conditioning history, which may lead to the resulting model being less sparse.


This process is usually referred to as backward search.

let SA[l i , r i ] be the interval corresponding to the suffixes in we compute RANK(T bwt , l i , c), the number of times symbol c occurs before l i and RANK(T bwt , r i + 1, c), the number of occurrences of c in we additionally store the starting positions C s of all suffixes for each symbol s in Σ at a negligible cost of σ log n bits.


When using the French ERC we figured out that no nested terms are found within the candidates.

the post-and pre-pruned settings are equivalent and thus MF equals frequency.


Data analysis revealed that we would consider many of the high ranked "false" candidates as MWE.

we extracted the top ten ranked terms, which are not annotated as MWE from the methods with the best P @100 performance, resulting to the log(freq) DRUID and the pre-pruned C-value methods.


(2013b) show that the task can be solved by finding a word that maximizes: where sim is a similarity measure, typically the cosine function.

levy and Goldberg (2014b) show that the Equation 1 is equivalent to: the goal of analogy detection is to find a word b* which is similar to b and a* but different from a.


and this is a central question in semantics; lexica and thesauri are built with the main purpose of answering this question.

nearest neighbor search in a DSM could be seen as a compilation step in a distributional lexicon.


The same is likely to be true for other perceptual modalities: in the case of comparisons such as guitar-piano, the auditory modal-  ity is certainly meaningful, whereas in the case of democracy-anarchism it is probably less so.

we had two graduate students annotate the datasets according to whether auditory perception is relevant to the pairwise comparison.


In order to accommodate the LLFs with syntactic types in LLFs of (Muskens, 2010), we extend the semantic type system with np, n, s, pp basic syntactic types corresponding to basic CCG cate-gories.

complex types are now built up from the set {e, t, np, n, s, pp} of types.


Note that 5-7 procedures are carried out manually while the phase is significantly facilitated by graphical proofs produced by LangPro.

6 there were collected around 30 new rules where about a third of them are admissible ones; the new rules cover phenomena like noun and adverbial modifiers, prepositional phrases, passive constructions, expletive sentences, verbparticle constructions, auxiliaries, light verb constructions, etc.


And these strong polar words have invariant polarity across domains.

the emotion can be substantialized by a series of emotional words drawn from corresponding probability distribution.


In this paper, we only select emotional words with strong sentiment polarities to represent the vector of the document.

we rebuild the data with the help of emotional lexicon cutting out the nonemotional words.


Therefore, we rebuild the data with the help of emotional lexicon cutting out the nonemotional words.

any word chosen from the rebuilt data will be an emotional word, which is supposed not to change its polarity in different domains.


However, we also wish to investigate how well individual annotators perform.

we also compute error and pairwise correlation for each individual annotator with the authors or other annotators, and then average over the pairwise comparisons for each annotator type.


For each pair of FCPs, we calculated the distance.

we characterized each facial expression by 68  67 / 2 = 2,278 distances.


Gloss Normalization Since the entries in AraMorph are bound to stems, the English glosses are inflected for number.

we lemmatize the English glosses in AraMorph in order to be able to match to the synset terms in SentiWordNet.


However, the training of log-linear neural language model is based on local word dependencies (e.g., the co-occurrence of the words in a local window).

paragraph Vector can not explicitly model the word dependencies for those words that do not frequently appear in a local window but are actually closely dependent on each other.


Although the three phrases are unconditionally dependent, their joint distribution can be conditionally factorized.

the dependency in both two examples can not be pure.


A sufficient and unbroken meaning of dependence can not only supply the context but also avoid the ambiguity (or noise) in global context.

the meaning of pure is important in such a global context modeling method.


The update procedure of v is as same as the procedure described in (Mikolov et al., 2013b).

the pseudo code for training TPD words is listed individually: Apriori (not a pure dependency method) is contrastively adopted to implement Glo-PV-DBOW.


It can be explained that the Apriori algorithm is short of an explicit statistical test procedure to guarantee the pure dependence.

the Apriori algorithm is not suitable for generating the high-order dependence.


From Table 3, we can see that the statistical information of corpus like words co-occurrence can be mined by TPD.

the Glo-PV-DBOW's embeddings are context-aware and it can help a lot for classification tasks.


Even when statistical techniques are applied to these tasks, their concentration has been on simple statistical significance tests and descriptive statistics (De Charms, 2013;Zhang et al., 2013).

we believe that statistical natural language processing techniques are needed for insightful analysis and interpretation in behavioral studies.


Unfortunately, when we applied these regular expressions on our dataset, they yielded only 16% recall for questioning and 14% recall for denying tweets.

this motivated us to seek a better approach to tweet-level classification.


First, we consider the Leave One Out (LOO) setting, which means that for each rumour r ∈ R, we construct the test set equal to T r and the training set equal to T \ T r .

this is a very challenging and realistic scenario, where the test set contains an entirely unseen rumour, from those in the training set.


However, statistical properties are more challenging in applying the distant supervision assumption than relations between named entities due to the fact that the numerical values are often approximated in text, as in the example of Figure 1.

linking the values mentioned in text with those in the knowledge base is not trivial and thus it is not straightforward to generate training instances for the property of interest.


The key difficulty compared to other applications of distant supervision is that numerical values are often approximated in text.

instead of looking for patterns that report the exact values for each entity, we develop an approach for finding the patterns that predict the values well.


Best performances were achieved for relations such as population which have a wide range of values that is well separated from the rest, while percentage rates were usually harder for the opposite reason.

we conclude that the algorithm with adjusted MAPE selects better patterns for each property that are encountered more frequently, which is important for the main goal of this paper, claim identification.


However, when doing QA over larger and more realistic KBs (and other tasks with similar characteristics), exhaustive enumeration of rules or reliable estimations of alignments are not possible, which prevents the application of tree transducers.

it is on the latter type of tasks where we focus our contribution.


For example consider the following paths which corresponds to the above syntactic structure: The lowest common ancestor of "their" and "risks" is NP.

r is added after NP in the path of "their" and L is added after NP in the path of "risks".


(2009), syntactic parsers do not perform well on the Genia treebank.

in our symmetry component we opted to not rely on predicted tree structures, and instead use the simpler option of representing each conjunct by its sequence of POS tags.


In other words, LEN is a stronger constraint than DEP in that the structures penalized by DEP are only a subset of structures penalized by LEN.

when LEN and DEP are combined LEN overwhelms, and the advantage of DEP is weakened.


Some OIE systems are: TextRunner (Yates et al., 2007) WOE (Wu and Weld, 2010): They use a sequence-labeling graphical model on extractions labeled automatically using heuristics or distant supervision.

long-range dependencies, holistic and lexical aspects of relations tend to get ignored.


It produces 1.7-1.8 times more minimal extractions and achieves 1.1-1.2 times higher informativeness than CLAU-SEIE.

far, we have tested our bootstrap learning and proposition linking approaches only on a small dataset.


As we have mentioned above, dependency tree is able to shorten the distances between pairs of marked entities and help trim off redundant words.

it is straightforward and reasonable to transfer the position feature from plain text to dependency tree.


For fair comparison, we add three features (POS tags, NER tags and WordNet hypernyms of marked entities) to the vector extracted by our model and retrain the network.

our model achieves an F 1 of 84.6 and outperforms all existing baselines in a fair condition where no data augmentation strategy is adopted.


We obtain the surprisal of an input word by taking the negative logarithm of the conditional probability of this word given the context words that precede it: As a consequence of skipping, not all input words are accessible to the reader network.

the probability and surprisal estimates it computes crucially only take into account the words that have actually been fixated.


The very high frequency of this single artificial construction (see other in Table 1) results in higher predictions of it for any of the verbs, even though the "transfer" feature in test inputs has a value intended to signal one of the dative constructions.

the preferences for the dative constructions in both models have a very small range of values, showing relatively small differences.


For the parameters of the generator θ g , Using the fact The last term is the expected gradient where the expectation is taken with respect to the generator distribution over rationales z.

we can simply sample a few rationales z from the generator gen(x) and use the resulting average gradient in an overall stochastic gradient method.


In this set-up, a fraction of the original question text should be sufficient to represent its content, and be used for retrieving similar questions.

we will evaluate rationales based on the accuracy of the question retrieval task, assuming that better rationales achieve higher performance.


Cloze-style questions are ideally-suited to this process, because inserting the correct answer at the placeholder location produces a well-formed, grammatical statement.

the correct hypothesis will "make sense" to a language model.


In many cases unique words repeat in a text.

we compute the total probability that word w is the correct answer using a sum: This probability is evaluated for each unique word in T .


This was done as follows: from the Extractor, we pass to the Reasoner's final GRU (i) the bidirectional hidden representation of the question; (ii) the bidirectional hidden representations of the end of each story sentence (recall that the Reasoner operates on sentence representations).

we reuse (parts of) the original biGRU encodings.


However, Levy and Goldberg (2015) note that there are limitations on the type of semantic knowledge which is encoded in these general-purpose similarity embeddings.

here we build customized task-specific embeddings for causal QA.


Even though the F 1 score of a logical form is 0, its answer type could still be correct.

we use the following trick: If there is a positive example with answer type t, we do not treat any other logical form with answer type t as negative example.


Specifically, EATS(x, y) depends on whether an arrow is present between x and y, which we must train a vision model to determine.

eATS should be a learned function of the environment.


It is clear that some characteristics of words (e.g., semantics) usu-ally remain the same across different roles.

it is practical to share some information across rolespecific weights so that the embeddings can benefit from more data and learn better semantic representations while leaving room for role-specific traits.


Since the output unit biases of the neural network model are independent from the inputs, they correlate strongly (r s = 0.74, p = 0.0) with training corpus word frequencies after being trained.

setting the learned output layer bias vector to a zero-vector is a simple way to reduce the effect of word frequencies on the model's output probability distribution.


S(w) denotes the set of all the senses of word w. We assume that the sets of senses of different words do not overlap.

in this paper a word sense can be seen as a lexeme of the word (Rothe and Schutze, 2015).


(2016) also suggest that SCWS should be of limited use for evaluating word representation models.

the results on this task shall be taken with caution.


Our experiments on the Penn Discourse Treebank (PDTB) (Prasad et al., 2008) show that these new representations deliver improvements over systems using these generic representations and yield state-of-the-art results, and this without the use of other hand-crafted features, thus also alleviating the need for external linguistic resources (like lexical databases).

our approach could be easily extended to resource-poor languages as long as connectives can be reliably identified on raw texts.


It is helpful to note that the parameters of attention and linear layers are shared in different hops.

the model with one layer and the model with nine layers have the same number of parameters.


We consider that each hidden vector of TDLSTM encodes the semantics of word sequence until the current position.

the model of TDLSTM+ATT actually selects such mixed semantics of word sequence, which is weird and not an intuitive way to selectively focus on parts of contexts.


However, in Table 4(b), the content-based model also gives a larger weight to "dreadful" when the target we focus on is "food".

the model incorrectly predicts the polarity towards "food" as negative.


And by using non-linear functions through multi-layer neural networks, these models represent a more expressive hypothesis space.

it would be interesting to explore how these neural network models could be extended for cross-domain sentiment classification.


The first hidden layers have recurrent connections from the past words while second one's direction of recurrent of connections is flipped, passing activation backwards in the texts.

in the LSTM layer, we can get the forward hidden state h i,j from the forward LSTM network and the backward hidden state h i,j from the backward LSTM network.


Suppose we have the sentence attention score A i for each sentence s i ∈ x, and the word attention score a i,j for each word w i,j ∈ s i , both of the scores are normalized which satisfy the following equations, The sentence attention measures which sentence is more important for the overall sentiment while the word attention captures sentiment signals such as sentiment words in each sentence.

the document representation r for document x is calculated as follows, Note that many LSTM based models represent the word sequences only using the hidden layer at the final node.


Note that our attention mechanism considers both the word embedding vector and the hidden state vectors.

the same word "easy" gets different scores in different positions.


The goal of multi-way, multilingual model is to build a neural translation model that can translate a source sentence given in one of N languages into one of M target languages.

to handle those N source and M target languages, the model consists of N encoders and M decoders.


to find the correct derivation, as multiple derivations may lead to the same solution.

the learning algorithm has to explore the output space to guide the model in order to match the annotated response.


Second, in many cases, even if we could find a semantic parse from responses, multiple combinations of templates and alignments could end up with the same solution set (e.g., the solutions of equations 2 + x = 4 and 2 × x = 4 are the same).

the implicit supervision signals may be incomplete and noisy, and using the solutions alone to guide the training procedure might not be sufficient.


Finally, since we need to have a complete derivation before we can observe the response of the mathematical engine E, we cannot design efficient inference methods such as dynamic programming algorithms based on partial feedback.

we have to perform exploration during learning to search for fully constructed semantic parses that can generate the correct solution.


4 As discussed in Section 3, the key challenge of learning from implicit supervision is that the mapping E(y) is one-directional.

the correct equation system cannot be easily derived from the numerical solution.


The FSC model shares the pointer network of the ASC model and combines a softmax output layer over the whole vocabulary.

while training on the sentencecompression pairs, it is able to balance copying a word from the source sentence with generating it from the background distribution.


Thus, we use the REINFORCE algorithm Mnih and Gregor, 2014) to reduce the variance of the gradient estimator.

the variational lower bound of the ASC model is: by optimising the lower bound (Eq.


In practise, the pre-trained language model prior p(c) prefers short sentences for compressions.

as one of the drawbacks of VaEs, the KL divergence term in the lower bound pushes every sample drawn Compresser Compression (Combined Pointer Networks) acting to regularise the posterior, but also to restrict the learning of the encoder.


For the parameters φ in the compression model, we firstly define the learning signal, Then, we update the parameters φ by: However, this gradient estimator has a big variance because the learning signal l(s, c (m) ) relies on the samples from q φ (c|s).

following the RE-INFORCE algorithm, we introduce two baselines b and b(s), the centred learning signal and inputdependent baseline respectively, to help reduce the variance.


In the context of this paper, we apply a variational lower bound (mixed reconstruction error and KL divergence regularisation) instead of the explicit Rouge score.

the ASC model is granted the ability to explore unlimited unlabelled data resources.


The representation of a sequence computed by bi-LSTMs is always a vector with fixed dimension despite the length of the sequence.

when dealing with a text span with hundreds of EDUs, bi-LSTM may not be enough to capture the whole semantic information with its limited output vector dimension.


For the backward span level LSTM, we do it the same way just in reversed order.

we decrease the computational complexity of computing the semantic representations for all possible span pairs which is the most time-consuming part of the original parsing process from O(n 4 ) to O(n 2 ).


So we can further cache the independent calculation results before Relu operation for each span.

we decrease the computational complexity of a large part of tensor-based transformation from O(n 3 ) to Word/POS Features One-hot representation of the first two words and of the last word of each span.


This is based on the observation that discriminative models are able to obtain promising results using only x.

assuming the discourse arguments encode the prior information for discourse relation recognition is meaningful.


Perhaps an even more fundamental difficulty is that it is not clear whether our current set of events' definitions is adequate .

given the complexity and fundamental difficulties, the current evaluation methodology in this area focuses on a limited domain of events, e.g.


33 types in ACE 2005 (NIST, 2005) and 38 types in TAC KBP .

this allows researchers to train supervised systems that are tailored to these sets of events and that overfit the small domain covered in the annotated data, rather than address the realistic problem of understanding events in text.


Moreover, with the same representation, we can evaluate event similarities and decide whether two event mentions are co-referent.

the proposed MSEP, can also adapt to new domains without any training.


Formally, we define S pair (a = 4 See supplementary materials for the full list of examples.

nIL) and S single (a = nIL) as follows: when we encounter missing event arguments, we use S pair (a = nIL) to replace the corresponding term in the numerator in S(e 1 , e 2 ) while using S single (a = nIL) in the denominator.


To balance between the effectiveness of ESA representations and its cost, we use the 200 concepts with the highest weights.

we convert each text fragment to a very sparse vector of millions of dimensions (but we just store 200 non-zero values).


We set the maximum tree depth to 20, and use a combination of path prefixes of length 4,6 and 10 as our BC representation.

we convert each word to a vector of 2 4 + 2 6 + 2 10 = 1104 dimensions.


Given a hyponym term hypo and k context words c 1 , c 2 , .., c k in the training triple, the output of hidden layer h is calculated as: where v t is the vector representation of the input word/term t. The weight of h in Equation 1is calculated as the average of the vector representation of hyponym term and contextual words.

this weight is not based on a fixed number of inputs.


In addition, our term embedding learning approach has the property that the embedding of hypernym is encoded based on not only the information of hyponym but also the information of contextual words.

we add one more feature to the input of SVM, i.e.


Another such example is shown in the Plant domain, where our ap-proach recognizes 'lily' as a hyponym of 'flowering plant', but WordNet places them in different subtrees incorrectly 3 .

our results may help restructure and even extend WordNet.


This model encodes not only the hypernym and hyponym terms, but also the contextual information between them.

the extracted term embeddings have good generalization capability to identify unseen taxonomic relations which are not even defined in dictionary and training data.


For our purposes, however, instead of learning a global linear transformation function in two spaces over a broad relation like hypernymy, we learn a function sensitive to a given do-main of knowledge.

our training data becomes restricted to those term-hypernym BabelNet sense pairs For each domain-wise expanded training set T d , we construct a hyponym matrix , which are composed by the corresponding SENSEMBED vectors of the training pairs Under the intuition that there exists a matrix Ψ so that y d = Ψ x d , we learn a transformation matrix for each domain cluster C d by minimizing: Then, for any unseen term x d , we obtain a ranked list of the most likely hypernyms of its lexicalization vectors x j d , using as measure cosine similarity: At this point, we have associated with each sense vector a ranked list of candidate hypernym vectors.


it is a projective dependency tree rooted in the node 0.

not all partial changes χ k (i,j) are possible to perform on graph G s .


The hyper-parameters of Dirichlet prior determine how much is the predictive distribution smoothed.

the predictive distribution for the word-in-role distribution can be expressed as 1 The most parent changes are possible for the special case of the tree, where each node i has parent i − 1.


Thus the predictive distribution for the word-in-role distribution can be expressed as 1 The most parent changes are possible for the special case of the tree, where each node i has parent i − 1.

for each node i we can change its parent to any node j < i and keep the projectivity of the tree.


For every training example (x, y) ∈ D, it holds that: ∪ z∈Y(x) m J (z, y) ⊆ Y(x).

as u separates the data with margin δ, it holds that: also δ JJx ≥ δ.


2015, on the contrary, improve a natural language inference task from an accuracy of 71.3% to 80.8% by initializing parameters with an additional dataset of 550,000 samples.

more systematic studies are needed to shed light on transferring neural networks in the field of NLP.


We have achieved reasonable performance that is comparable to similar models reported in the literature with all six datasets.

our implementation is fair and suitable for further study of transfer learning.


Existing evidence shows that frozen parameters would generally hurt the performance (Peng et al., 2015), but this setting provides a more direct understanding on how transferable the features are (because the factor of target domain optimization is ruled out).


The curves show that the non-projective searching mode fall behind of the projective one, this is because the dependency structures extracted from constituency trees are projective, and the projective search mode implies appropriate constraints on the searching space.

we use the projective searching mode for the evaluation of the original grammar.


Thus, when we used DCU as baseline, the best correlation and nDCG were achieved.

we must determine the appropriate baseline system before the comparisons.


with two lists of candidate answers, we can apply an optimization method as below to select the best (B, D) pair, where T 1 , T 2 are two lists of candidate words.

we get the answers B and D that can form an analogical relation between word pair (A, B) and word pair (C, D) under a certain specific word sense combination.


The candidate word that is not similar to others does not have similar cooccurrence information to other words in the training corpus, and thus this word should be far away from other words in the word embedding space.

we first calculate a group of mean vectors m iw 1 ,••• ,iw N of all the candidate words with any possible word senses as below , where T is the set of candidate words, N is the capacity of T , w j is a word in T ; is the index for the word senses of w j , and is the number of word senses of w j .


Therefore we first calculate a group of mean vectors m iw 1 ,••• ,iw N of all the candidate words with any possible word senses as below , where T is the set of candidate words, N is the capacity of T , w j is a word in T ; is the index for the word senses of w j , and is the number of word senses of w j .

the number of the mean vectors is M = N j=1 k w j .


For the first solver, we also leverage the property that words with similar cooccurrence information are located closely in the word embedding space.

given the question word w q and the candidate words w i , we can find the answer by solving: where T is the set of candidate words.


In addition, most of the online tests only calculate the final IQ scores but do not provide the correct answers.

we only use the online questions to train the verbal question classifier described in Section 3.1.


The model processes text incrementally while learning which past tokens in the memory and to what extent they relate to the current token being processed.

the model induces undirected relations among tokens as an intermediate step of learning representations.


The model maintains two sets of vectors stored in a hidden state tape used to interact with the environment (e.g., computing attention), and a memory tape used to represent what is actually stored in memory.

1 each token is associated with a hidden vector and a memory vector.


We employ a semi-automated approach for the conversion from graph query to natural language question, which provides two levels of paraphrasing: Common lexical forms of an entity (e.g., "Queen Elizabeth" and "Her Majesty the Queen" for ElizabethII) mined from the Web are used as entity paraphrases, and the remaining parts of a question are paraphrased by annotators.

dozens of paraphrased questions can be produced for a single graph query.


Due to the inadequacy of current search engines to answer advanced questions, users may adapt themselves accordingly and mostly ask simple questions.

questions collected in this way, to some extent, may still not well reflect the true distribution of user information needs, nor does it fully exploit the potential of KBbased QA.


Despite the effectiveness of those methods, it is still challenging to discriminate different sentiment polarities at a fine-grained aspect level.

we are motivated to design a powerful neural network which can fully employ aspect information for sentiment classification.


In this way, the output hidden representations (h 1 , h 2 , ..., h N ) can have the information from the input aspect (v a ).

in the following step that compute the attention weights, the inter- Aspect Embedding dependence between words and the input aspect can be modeled.


In addition, W h , W v , W p , W x , w are the parameters of attention.

the additional parameter set of AT- AE-LSTM: The parameters include the aspect embedding A.


Besides, the dimension of W i , W f , W o , W c will be expanded since the aspect vector is concatenated.

the additional parameter set consists of {A}.


An advantage of using dependency tree over the other is the ability to extract word-level representations considering syntactic relations and semantic robustness.


At the second position, the input features for like are composed of the hidden vectors at position 1 (h I ), position 2 (h like ) and position 3 (h the ).

the conditional distribution for the entire sequence y in Figure 1(c) can be calculated as where the first three terms in the exponential of the RHS consider unary clique while the last term considers the pairwise clique with matrix V representing pairwise state transition score.


During training, RNCRF computes the hidden vector h like for like, which is also obtained from h food .

the prediction for like is affected by h food .


This is the other-way propagation from like to food.

the dependency structure together with the learning approach help to enforce the dual propagation of aspect-opinion pairs as long as the dependency relation exists, either directly or indirectly.


This enforces the topic distribution to tend towards these terms over time as frequency of them increases.

the posterior of generative topic models often favors terms with high frequency e.g., unigrams, while phrasal terms are ranked lower due to their lower frequencies.


We optimize the collocations of relevant aspect words and phrases in the GPU framework in two ways: Word to phrase: Intuitively, if an aspect word is assigned to a topic then that topic should represent that aspect and to all other phrases in that aspect's phrase set (i.e., phrases containing that aspect) should belong to the same topic.

when an aspect word is assigned to a topic then each phrase in its aspect set is promoted with a small count in that topic.


As mentioned in section 1, some emotions often co-occur such as joy and love, and some rarely co-exist such as joy and anger.

the third term is employed to incorporate such prior knowledge.


The emotions with the intensity value higher than y 0 's are regarded as the relevant emotions, and the rest emotions are regarded as irrelevant ones.

eDL in fact implements the function of MLL without the need of setting the threshold manually.


We were unable to build an IRT model that accurately modeled ability to recognize entailment with the obtained response patterns.

no items from the 4GS entailment set were retained.


Then, by lettingΞ := ξ w,c (w,c)∈Ω wherê we have J Û ,V,Ξ = J Û ,V .


Since ρ (•) is concave, its gradient ρ (•) is monotonically non-increasing (Rockafellar, 1970).

when rank (w, c) and hence ξ −1 w,c is large, ρ ξ −1 w,c is small.


In this paper, the SWE model is utilized as a tool to explore the semantic relevance of brain activity by examining the performance of the estimated word embeddings after integrating brainactivity-related knowledge.

we construct the ranking inequalities in Eq.


Then, we clustered the SIFT features into 1000 classes to construct the visual vocabulary, and each image was divided into 8 regions.

the BoVW representation of an image was a vector of 8000 dimensions.


The number of scales and orientations were set to 5 and 8, respectively.

we represented the primary visual features of each noun as a vector of 163,840 dimensions.


In practice, the search space contains a large number of invalid partial candidates.

we introduce three partial output pruning schemes which are helpful in reducing search space as well as making the input for parameter update less noisy.


Admittedly, the joint decoding problem is more intricate and in general intractable.

we use a beam-search-based incremental decoder for approximate joint inference during training and testing.


This makes searching through the collection difficult and generally less accurate.

the social annotation or bookmarking system demonstrates an extreme example of the vocabulary mismatch problem encountered in personalized web search.


Since every part is deleted at most once, and gets updated (its arcs are added to the tree) at most k times, the total DS update time is O(k × |parts|) = O(n k+1 in ).

algorithm 1 runs in O(|parts| × k + n 2 × n in × (n in + min{n k−1 in , n 2 in })) time.


In the i-th iteration we add e = (u, v) to T i , and remove the lostArcs from E. Every lost arc participates in O(n k−1 in ) parts, and we need to update O(k) entries for each lost part in DS(a) (as the value of the other arcs of that part should no longer account for that part's weight) and O(k 2 ) entries in DS (b).

the total update time of the DSs is O(n k in × k 2 ) and the total runtime of Algorithm 2 is O(n k+1 in × k + n × (n × n in × (n in + min{n 2 in , n k−1 in })+n k in ×k 2 )).


Since these works usually use character level features, meaningless text segments are one of the major error types.

we integrate the dynamic Chinese dictionary into this method to detect and discard meaningless text segments.


This leads to missing or low quality embeddings for such inflectional forms, even for otherwise frequent verbs, i.e., sparsity is a problem.

we propose to compute normalized embeddings instead of embeddings for surface/inflectional forms (referred to as forms throughout the rest of the paper): STem EMbeddings (STEM) for word stems and LemmA eMBeddings (LAMB) for lemmata.


For   natural choice as corpus, because it is available for many languages.

we use the preprocessed and tokenized Wikipedia datasets of .


We have |T i | = r 2 without considering the accumulated probability threshold λ.

it requires O(nr 4 ) time complexity to compute E t|x,S;θ [f(x, t)] using the Forward-Backward algorithm.


The reason for the undesirable performance is that the topics of event entries in the comprehensive reference summary are not focused, which results in very few reference (positive) examples for each topic.

the miscellaneousness of topics of positive examples makes them tend to be overwhelmed by large numbers of negative examples during training the model, leading to very week supervision and making it difficult for AreaRank to learn the patterns  of positive examples.


Providing such rationales during manual classification is a natural interaction for annotators, and requires little additional effort (Settles, 2011;McDonnell et al., 2016).

when training new classification systems, it is natural to acquire supervision at both the document and sentence level, with the aim of inducing a better predictive model, potentially with less effort.


One nice property of our model is that it naturally provides explanations for its predictions: the model identifies rationales and then categorizes documents informed by these.

if the model classifies a test instance as positive, then by construction the sentences associated with the highest p ij pos estimates are those that the model relied on most in coming to this disposition.


Even websites aiming for the same purpose often violate this assumption, let alone websites of different types.

we expect that the source and target services contain heterogeneous information (e.g.


In our model, we make a less strong assumption: regardless of the type of information available in each domain, the users that are similar in one domain should have similar taste in the other domain.

instead of directly transfer the content material from source to target domain, we transfer the similarity between users, and use it as a guide to improve the CF model in the target domain.


That is, we are only provided the Twitter corpus when making prediction for these users.

our previous strategy of averaging the similarities only applies to users in the training set.


However, in the NHPMF model, the mean parameters are not treated as constants when the user latent factors are updated.

an extra term appears in the gradient formula, which leads to an O(k 2 ) time complexity, with k being the number of nearestneighbors considered.


Moreover, to examine the robustness of our CNN-based model towards different text types within biomedical domain, all the models are trained on the same Abstracts sub-corpus.

the results on Abstracts can be regarded as in-domain evaluation while the results on Clinical Records and Full Papers can be regarded as cross-domain evaluation.


We have noticed that most negative cues (e.g., "not", "no", "without", "fail") are often applied to negate phrases, and the tokens in negation scope tend to have the tight dependency relationship with them.

our model can achieve better results using dependency paths for negation scope.


When a hashtag appears inside a tweet, it is most likely to be an inseparable semantical part of the tweet and has important meaning.

we regarded this hashtag as a keyphrase of the tweet.


On the other hand, we need to integrate these metaphors into the riddle descriptions to generate riddles.

how to extract the metaphors of riddles becomes a big challenge in our task.


We replace this radical to a placeholder and generate a candidate rule with the corresponding direction by the radical position in this character.


The hidden size is varied in such a way that the number of trainable parameters are close to 3.55 million parameters.

the Approx skip chain CRF has 200 hidden layer size, while standard Bi-LSTM model has 250 hidden layer.


Its vocabulary is close to two other labels: ADE (1,807 incidences) and the most populous Other SSD (40,984 incidences).

it is harder to separate the three labels.


With the help of tensor factorization, it could collectively embed the information of different relations into the final representations of reviews, and further optimize the representations.

it could faithfully reflect the original characteristics of the entire review system with a global manner.


To unify the values of different matrices to a reference system, we normalize with the sigmoid function.

the value '0' will be normalized to '0.5'.


It removes the noise of the original data by learning through the global loss function.

we get the representation of reviewers and products with a further optimization by the collective learning.


of the effort that went into the English system, we could build a system for German covering 89% of the rule set.

we present PropsDE, the first Open IE system for German.


If we rewrite the two-layer architecture in a compact way, we obtain: As the equation suggests, if we minimize the negative log likelihood, the loss function is not convex.

we could land in a non-optimal local minimum using online learning.


At train time, the parameters of the LSTM are learned end-to-end to minimize this asymmetric ordered loss (as measured over the gold-standard sequences).

at test time, we use S( as we move away from the origin in the embedding space, we traverse through the sentences in a story.


We design and test multiple game-inspired novel attention-annotation interfaces that require the subject to sharpen regions of a blurred image to answer a question.

we introduce the VQA-HAT (Human ATtention) dataset.


A potential source of center bias is the fact that the VQA dataset was human-generated by subjects looking at images.

salient objects in the center of the image are likely to be potential subjects of questions.


HieCoAtt-Q now has higher correlation with human attention maps than Judd saliency.

discounting the center bias, VQA-specific machine attention maps correlate better with VQA-specific human attention maps than task-independent machine saliency maps.


It does not use explicit representations for entire segments and is incapable of incorporating simple lexical information, e.g., does this canonical segment occur as an independent word in the lexicon?


Since such semantic similarity is but one of the components of context incongruity and since existing feature sets rely on sentiment-based features to capture context incongruity, it is imperative that the two be combined for sarcasm detection.

our paper deals with the question: Can word embedding-based features when augmented to features reported in prior work improve the performance of sarcasm detection?


For all similarity scores, we divide them by square of distance between the two words.

the similarity between terms that are close in the sentence is weighted higher than terms which are distant from one another.


Thus, the similarity between terms that are close in the sentence is weighted higher than terms which are distant from one another.

for all possible word pairs, we compute four features: • Maximum distance-weighted score of most similar word pair • Minimum distance-weighted score of most similar word pair • Maximum distance-weighted score of most dissimilar word pair • Minimum distance-weighted score of most dissimilar word pair These are computed similar to unweighted similarity features.


These improvements are not directly comparable because the four embeddings have different vocabularies (since they are trained on different datasets) and vocabulary sizes, their results cannot be directly compared.

we take an intersection of the vocabulary (i.e., the subset of words present in all four embeddings) and repeat all our experiments using these intersection files.


Each gain value is averaged over the four prior works.

when unweighted similarity (+S) based features computed using LSA are augmented to features from prior work, an average increment of 0.835% is obtained over the four prior works.


But the target of the opinion in the tweet may not necessarily be him, but related targets, such as Hillary Clinton and Ted Cruz.

automatic detection of targets and inferring the stance towards all of the targets is the next step toward creating a practical weakly-supervised stance classifier.


A subreddit as a whole is characterized by a distribution of topics as learned via LDA, but any particular discussion thread would not necessarily reflect the full distribution.

we characterize each subreddit with multiple topic vectors.


Then, this token's children lose their head in the transfer process.

we rescue these children by attaching them to the head of "are", which, in this case, is ROOT token.


In Figure 1(a), the words "made" and "slipped" in the ASR hypothesis do not match the gold transcription tokens, "may" and "flip", respectively.

we automatically re-label the arc from each token to its head as "error".


During decoding the processing time increases linearly with the length of the audio input segment.

the largest time contribution is due to the computations inside the network (He and Sun, 2015), which with a single convolution layer can be performed in negligible time for single utterances.


A further drawback of manual feature templates is that they are sparse, instantiated by discrete pattern-matching.

parsers and semantic analyzers are necessary as a preprocessing step to offer syntactic and semantic patterns for feature extraction.


(See Section 6.1 for details on dataset creation).

we develop an algorithmic approach for predicting projective equation trees, and show empirically that it compares favourably with ones which do not make the projective assumption.


As many of these dialects have traditionally existed primarily in oral contexts, they have historically been underrepresented in written sources.

nLP tools have been developed from text which aligns with mainstream languages.


A token t's latent topic z t is drawn from θ m , and the word itself is drawn from φ zt , the language model for the topic (Figure 1).

the model learns demographically-aligned language models for each demographic category.


This new use of though as a discourse marker, which is difficult to observe using the SAE spelling amidst many instances of the SAE usage, is readily identifiable in examples containing the thoe variant.

nonstandard spellings provide valuable windows into a variety of linguistic phenomena.


Hypothesis I: For Hi-En bilinguals, Hindi is the preferred language for expression of opinion on Twitter.

we expect i.e., pr(⊗|hd; T ) < pr(⊗|er; T ) And similarly, pr(⊗|hr; T ) < pr(⊗|er; T ) Hypothesis II: For Hi-En bilinguals, Hindi is the preferred language for expression of negative sentiment.


Therefore, we expect i.e., pr(⊗|hd; T ) < pr(⊗|er; T ) And similarly, pr(⊗|hr; T ) < pr(⊗|er; T ) Hypothesis II: For Hi-En bilinguals, Hindi is the preferred language for expression of negative sentiment.

in particular, we would like to hypothesize that the odds of choosing Hindi for negative over positive is really high compared to the odds for English.


Hypothesis Ia: Hindi is the preferred language for expression of opinion in Hi-En code-mixed tweets.

we expect i.e., pr(⊗|m h r; T ) < pr(⊗|m e r; T ) Hypothesis IIa: Hindi is the preferred language for expression of negative sentiment in Hi-En codeswitched tweets.


hr tweets are from general users and show similar trends as English.

in general, there seems to be no preferred language for expressing opinion by the Hi-En bilinguals on Twitter.


3b are already good approximations of the target distribution, while the weights of the single-word δ j distributions are not yet able to provide accurate probabilities.

the model learns to set the mixture proportions of the δ elements to near zero and rely mainly on the count-based n-gram distributions.


We solve this by generating the whole sonnet in reverse, starting from the final rhyme word.

when we expand the state L1-S8, we can choose from almost every word in vocabulary instead of just b possible words.


The probability estimates of those models are smooth functions of these embeddings, and a small change in the features results in a small change in the probability estimates (Bengio et al., 2003).

neural language models can achieve better generalization for unseen n-grams.


Linguists generally agree that the antecedent for sluicing is a sentential constituent (see Merchant (2001) and references therein).

it is straightforward to define the antecedent as the minimal sentence-level constituent containing the token sequence marked as the antecedent.


Even for correlate-less sluices, the WH-phrase must semantically cohere with the main predicate of the antecedent.

in (13), S-3 is a more likely antecedent than S-2 because increase is more likely to take an implicit extent than predict.


Rather, they are about how well a given predication fits with a given type of question.

instead of using our comparatively small set of annotated sluicing examples, we used overt WH-constructions in Gigaword to observe cooccurrences between question types and main predicates.


In addition to the target-language sentence e, our model hypothesizes a sequence f = f 1 • • • f l of source-language clusters (intuitively, sourcelanguage words), and spans (a i , b i ) of the source signal that each target word e i is aligned to.

the clusters f = f 1 • • • f l and the spans a = a 1 , .


Then we can define: where DTW is the distance between the prototype and the segment computed using Dynamic Time Warping.

s assigns highest probability to spans of φ that are most similar to the prototype φ f .


To speed up training (with nearly no change in accuracy, in our experiments), we restrict this relation so that there are k source clusters for each target word, and a source cluster uniquely determines its target word.

t(e | f ) is fixed to either zero or one, and does not need to be reestimated.


Using more sentences provides additional information about the entity which might be helpful, but it is also more likely to introduce noisy information which could affect the classification accuracy adversely.

we use the first 200 tokens of the text in a Wikipedia page and create n-gram word features out of them.


We have also found that the length of an entity name (i.e., number of words in an entity name) also plays an important role for determining which entities should be included in the mapping for improving an NER system.

we use English-Wiki-Mapping(t, i) to denote the English Wikipedia entity type mapping that includes all the entities with confidence scores greater than or equal to t and at least i words in their names.


The dominant approach to training a seq2seq system is as a conditional language model, with training maximizing the likelihood of each successive target word conditioned on the input sequence and the gold history of target words.

training uses a strictly word-level loss, usually cross-entropy over the target vocabulary.


For beams of size K tr = 2, 3, 4, 5, and 6, our implementation processes on average 1,985, 1,768, 1,709, 1,521, and 1,458 tokens/second, respectively.

we appear to pay an initial constant factor of ≈ 3.3 due to the more complicated forward and backward passes, and then training scales with the size of the beam.


Run-time complexity for beam search grows linearly with beam size.

the fact that sequencelevel knowledge distillation allows for greedy decoding is significant, with practical implications for running NMT systems across various devices.


These three primaries would form the score vector, which is shared amongst all heuristics, is s = [100, 82, 76].

p = {( is an ), ( at of ), ( on as )}, and |p | = 3.


During the iterations, the initial labeled nodes X L may fade away with other labels.

we forcefully restore their actual label by setting y il = 1 (if x i ∈ X L is originally labeled as l), and other entries (∀ j =l y ij ) by zero.


However, this approach does not guarantee the label proportion observed in the annotated data (which in this case is not well-separated as shown in Section 4).

we adopt a label-based normalization technique.


by construction,T ij ≥ 0, and sinceT is row-normalized, andT uu is a part ofT , it leads to the following condition: the sum of each row inT n uu ij converges to zero, which indicatesT n uu ij Y 0 ← 0.


We assume that although the full text of P i is given, we do not have access to the full text of P j (may be due to the subscription charge or the unavailability of the older papers).

we consider only the title of P j as a proxy of its full text.


The hardest challenge in this task is that there is no publicly available dataset where references are annotated with the intensity value.

we constructed our own annotated dataset in two different ways.


Most existing methods (Yogatama et al., 2015;Ling and Weld, 2012) treat every type label in a training mention's candidate type set equally and independently when learning the classifiers but ignore the fact that types in the given hierarchy are semantically correlated (e.g., actor is more relevant to singer than to politician).

the learned classifiers may bias  Table 1: A study of label noise.


For example, MLN needs grounding for each candidate formula, i.e., it needs to enumerate all paths.

the computation complexity of MLN increases exponentially with the scale of a KB.


In more detail, to achieve such a goaldirected mechanism, at each step of random walk, the algorithm dynamically estimates the potentials for each neighbor by using the ultimate goal, and assigns higher probabilities to the neighbors with higher potentials.

the algorithm is more inclined to visit structures which are beneficial to infer the target and avoid transferring to noise structures.


C f is calculated as follows, where N f is the total number of instantiated formula f and N X is the total number of instantiated X. Enumerating paths is a time consuming process and does not apply to large-scale KBs.

random walk on the graph is proposed to collect frequent paths instead of enumerating.


To estimate a formula f , the algorithm employs f 's occurrence number during random walks N f to approximate the total number N f in Equation 1, and similarly employs N X to approximate N X .

f 's confidence C f can be approximatively estimated by N f and N X , noted as C f .


After updating Φ r ij and Φ r ik by the gradient of L inf rw , random walk is more likely to find high-quality paths but not noise.

the goal-directed random walk increases efficiency of mining benefic formulas and has a great capability of resisting noise.


At each step, the algorithm estimates attentions for each neighboring edges by Ψ.

there are several existing expressions of Ψ, e.g., the dot product (Sukhbaatar et al., 2015) and the single-layer perceptron (Bahdanau et al., 2015).


24, and Hispanic (10) (multiple ethnicities were allowed).

our recruitment yielded demographically diverse participants in ways that are useful for team research.


In addition to personality, we also incorporate basic human values since framing effects were also shown to be related to personal beliefs and motivations.

we have significantly increased the scope of our study over prior research.


For good assignments this quantity should be low.

we add this as a penalty to the objective.


Our preliminary study suggests that simply augmenting the traditional surface features with the distributed representations barely improves the performance of these entity linking systems.

we propose NTEL, a Neural model for Tweet Entity Linking, to leverage the distributed representations of authors, mentions, and entities.


We exclude any second-order features between entities.

for each mention candidate, we only need to decide whether it can take the highest scored entity candidateŷ t or the special Nil entity based on whether it is overlapped with other mention candidates.


Note that the number of parameters in our composition model is large.

we include an L2 regularizer on these parameters, which is omitted from Equation 11 for brevity.


Using phonological character representations instead yields an improvement of +0.47 F1 and a further +0.8 F1 with attention.

phonological representations benefit more from attention applied over them to beat out orthographic representations in that scenario.


The state of environment is an important notion in robotic systems as the change of states drives planning for lower-level robotic actions.

connecting language concepts to state changes, our learned AoG provides a unified representation that integrates language and vision to not only support language-based communication but also facilitate robot action planning and execution in the future.


2 We use the CMU's Phoenix parser: http://wiki.speech.cs.cmu.edu/olympus/index.php/Phoenix ment between these two sequences is also extracted from their co-occurrence timing information.

an instance of a task demonstration is formally represented as a 3-tuple .


A terminal node in the AoG represents a primitive action, which causes the object to directly change from one state to another.

we represent a terminal node as a 2-tuple of states (or a "change of state").


And s r is the end state of another existing node, whose initial state is s m .

an Andnode candidate always has two child nodes, and represents a pattern of sub-sequences which starts from state s l , ends at s r , and has s m occurred somewhere in the middle.


In order to be useful, the set Y of hypotheses must provide an accurate summary of the score landscape.

the hypotheses should be plausible (i.e., high-scoring) and mutually non-redundant (i.e., diverse).


Recall that our key focus is consistency -correct hypotheses from different modules will be correct in a consistent way, but incorrect hypotheses will be incorrect in incompatible ways.

our goal is to search for a pair (semantic segmentation, sentence parsing) that is mutually consistent.


Importantly, the slack (or violation in the margin) is scaled by the loss of the tuple.

if there are other good pairs not too much worse than the oracle, the margin for such tuples will not be tightly enforced.


In tagging, the model does not need to learn all facets of each word's semantics; it only needs to map a word to its syntactic categories.

simple surface-level features like affixes can help tremendously.


Unlike in applications like entity-tagging and parsing where the length of the output is determined based on the input, in applications like response generation valid outputs can be of widely varying length.

pr(y|x, θ) should be well-calibrated over all sequence lengths.


The syntactic prediction/parsing performance will increase due to such non-linearity.

we do not make conclusions based only on absolute performance values, but also on a comparison against the designed baseline models.


In this paper, we have proposed a method to incorporate discrete probabilistic lexicons into NMT systems to solve the difficulties that NMT systems have demonstrated with low-frequency words.

we achieved substantial increases in BLEU (2.0-2.3) and NIST (0.13-0.44) scores, and observed qualitative improvements in the translations of content words.


One might hypothesize that our gains come from exploit-  ing the English half of the corpus as an additional language model resource.

we explore transfer learning for the child model with parent models that only use the English side of the French-English corpus.


As is the case with any system where content is generated by web users, the generated content would be of varying quality, reliability, readability and abstraction.

manual curation of such datasets is inevitable to weed out low quality and duplicate content to ensure user satisfaction.


On the other hand, the solutions suggested in QA3 and QA4 are distinct and different legitimate solutions to the same problem cause.

from a semantics perspective, it is intuitive that QA3 and QA4 should be part of the same cluster in any clustering of the CQA dataset to aid actioning on them together; a human expert might decide to merge the question parts and tag one of the answers as an alternative answer.